A typical strategy for single-channel SE frontend is multi-condition learning [1]} and multi-task learning [2]}. Kinoshita et al. proposed the use of Conv-TasNet [3]} as SE frontend, and trained the ASR backend using both noisy and enhanced speech. Whereas this method freezes the SE frontend while training the ASR backend, Eskimez et al. proposed multi-task learning for the SE frontend using frozen pre-trained ASR backend [2]}. These methods suggest that the SE frontend needs to be trained using both SE and ASR losses, and the ASR backend needs to be trained using not only noisy speech, but also enhanced speech in various conditions. Along similar lines, the proposed method uses signals enhanced by various target SNRi as input to the ASR backend, and can, therefore, be viewed as a data augmentation based multi-condition learning strategy that avoids overfitting the ASR backend on noisy and/or fully-enhanced speech.
